Uses and Misuses of Price's Law or Pareto Principle

The notion that a five percent to 10 percent reduction in force at a large organization might be "productivity-neutral" or even "productivity-positive" rests on the premise that large organizations eventually suffer from organizational entropy or “slack.”

In massive corporations, the individual contribution of an individual is notoriously difficult to measure, if it can be measured at all.

The Pareto Principle (80/20 rule) suggests that 80 percent of the value is produced by 20 percent of the employees. If this holds true, a 10-percent layoff that misses the "vital 20 percent" would, mathematically, have a negligible impact on total output.

Price's Law likewise suggests that half of organizational output is created by just 10 percent of workers.

But the idea can be carried too far. In other words, Pareto might suggest  where value is concentrated, but it does not tell us what parts of the value chain, in what quantities, we can try to remove.

In other words, complex products often require many value chain contributions whose contributions are outside the “80 percent of value” attribution, but are still essential for product success. 

The logic of eliminating most of the other value chain elements outside the “80 percent of value” only works only when inputs are independent and optional. That is rarely, if ever, the case for most products. 

For example, a particular  product ships only if every step is completed. So even low-value steps are non-optional constraints. 

Also, some value chain operations represent option value or risk reduction. They might not show up in the completed product, but might instead provide protection against product failure. 

Quality assurance efforts, regulatory compliance or maintenance might not be direct value creators, but might be necessary to deliver a final product. 

So many functions might be structurally necessary but individually have low marginal impact. And even that does not help address the question of staffing levels to support such processes. 

Were that not the case, competitive markets would force firms toward the Pareto-suggestion of minimal staffing. And we do not see that. 

Study / Source	Domain	Key Finding	Implication for Workforce
Pareto principle overview	Operations / Engineering	80% of outcomes driven by ~20% of causes	Output concentration is real
Pareto in supply chains (Slimstock)	Supply chain	20% of products drive most revenue	Inventory focus is uneven
Lean operations Pareto usage	Manufacturing	Majority of defects from small set of causes	Useful for prioritization
Juran Pareto analysis guide	Quality management	“Vital few and useful many” distinction	Many low-impact roles still necessary
IMD Pareto analysis strategy article	Strategy	Pareto helps focus leadership effort, not eliminate complexity	Tool for prioritization, not simplification
Value chain simulation research (VCS)	Manufacturing systems	Output depends on interdependent processes across cost, quality, delivery	System requires multiple linked roles
Pareto distribution empirical study (arXiv)	Economics/statistics	Heavy-tailed distributions common in real systems	Inequality of contribution is structural, not eliminative

That noted, Pareto does make sense for:

• Prioritizing improvements (fix top 20 percent of defects)

• Sales focus (top 20 percent of customers or products)

• Time allocation (focus on high-leverage activities)

Still, Pareto notwithstanding, large organizations might often be less productive than imagined because of:

• Social Loafing: In large groups, individuals often work less hard than they would alone because their lack of effort is easily hidden by the group's overall performance

• Bureaucratic Friction: Beyond a certain size, organizations require "coordinators for the coordinators." Removing a layer of these roles can actually speed up decision-making, allowing the remaining staff to be more productive because they spend less time in meetings.

So when an organization has excess personnel, it can often absorb a reduction in force without losing core output, effectively "trimming the fat" to improve the output-per-employee ratio.

Study / Source	Key Focus	Source Link
Love & Nohria (2005)	Reducing Slack: Found that downsizing improves performance specifically when the firm has "excessive" resources (high slack).	Read at ResearchGate
Cascio, Young, & Morris (1997)	Financial Consequences: A landmark study showing that layoffs alone rarely boost ROA, but asset restructuring combined with cuts does.	Read at ResearchGate
Guthrie & Datta (2008)	Industry Context: Demonstrates that the negative impact of layoffs is significantly higher in "knowledge-intensive" (R&D) industries.	Read at ResearchGate
McKinsey & Company	The Productivity Imperative: Analyzes how technology and "de-layering" (removing management tiers) can boost service-sector productivity.	Download PDF (McKinsey)
Zyglidopoulos (2005)	Corporate Reputation: Examines how the market and stakeholders perceive downsizing as a signal of "efficiency-seeking" behavior.	Read at ResearchGate

The caveats are several.

Guthrie and Datta warn that in organizations with high innovation requirements, a 10-percent cut can remove critical "intangible assets" whose loss will not be seen until later. 

The Cascio study suggests that many firms fail to see long-term productivity growth after layoffs because they lose the ability to innovate

In other words, if the ideal is “cutting fat but not muscle,” the danger is “cutting some muscle as well.”.

If “productivity” is defined as total output divided by total input, then a smaller denominator “automatically” raises productivity, assuming output remains the same. 

The argument is that many large firms have enough "operational slack" (excess resources) that a five-percent to 10-percent  cut acts as a "forcing function," requiring the remaining staff to automate or abandon low-value tasks.

Large organizations are complex, so it might not be easy to determine how much, and where, to make layoffs. If one assumes every existing function actually is essential, then an “across the board” approach actually makes some sense.

The organization keeps the function, but possibly operates more efficiently. 

Price's Law: 10% of People Produce 50% of Outcomes

Price's Law states that half of the literature on a subject will be contributed by the square root of the total number of authors publishing in that area.

Extrapolated to organizational output, Price’s Law suggests 10 percent of people produce half the outcomes, while 90 percent produce the other half. 

In principle, it is similar to the Pareto theorem, which states that 80 percent of outcomes are produced by 20 percent of the actions. 

source: Darius Foroux 

And remember it is a square root or power law function: the disparities grow larger with scale. The percentage of people producing half the value actually decreases with scale. The Pareto theorem, for example, is linear. It suggests 20 percent of actions produce 80 percent of value, at any scale. 

source: ANG Traders 

Price’s Law is different. As population size grows, though the number of those contributing half the value grows, they grow at ever-decreasing rates in relation to the total number of associates. 

source: Semantic Scholar 

That is one reason why very-large organizations contain so many people who are apparently not functioning at a high level. 

A "Lazy" AI Narrative, Indeed

With the caveat that the statement is in accordance with his firm’s core business value, Jensen Huang is right when he says blaming artificial intelligence for mass layoffs is a “lazy narrative” used by executives to reframe older business problems.

Instead, such layoffs are about redeploying assets. Granted, the redeployment is to support spending on AI infrastructure. 

But the moves are about capital allocation, not AI job replacement; not yet. Overhiring during the Covid pandemic is more accurately the case, as firms now are rebalancing workforces that had become “bloated.”

Consider Price’s Law. 

You might not believe there is a logic to applying Price’s Law to significant large-organization layoffs, but there is a clear rationale. 

Price’s Law suggests that in any complex social system, achieving equality of outcome and equality of opportunity simultaneously is fundamentally impossible.

Applied to work groups and teams, Price’s Law suggests that the square root of the number of people in a domain creates 50 percent of the value:

• In a company with 100 employees, 10 people produce half the output

• In a field with 10,000 scientists, 100 produce half the meaningful research

• On a team of 25, 5 people carry the entire operation. 

source Niels Bohrman 

At least in principle, in a sufficiently-large entity, reducing headcount by 10 percent might not reduce total output much, if at all. 

Price’s Law is like the Pareto Principle (“80/20 rule”): a relatively small number of actors or actions produces a disproportionate percent of total value in any company, process or value chain. 

examples of Price’s law:

• Wealth distribution: The “Global Wealth Report” by Credit Suisse says 1.1 percent of the world’s population (56 million people) holds about 46 percent of the world’s total wealth

• Savings: A small number of people will have 50 percent or more of the total savings in a society, while most people will have very little — if any — savings

• World news: A fraction of world events makes up the vast majority of the news reported on in the media

• Number of books sold: A few authors will sell the vast majority of copies (Stephen King, J. K. Rowling, etc.). This can also be seen with music records, movie scripts, paintings, or any other creative product

• Classical music: Supposedly, 50 percent of the repertoire of classical music was composed by five composers — Bach, Mozart, Beethoven, Brahms, and Tchaikovsky

• Sports: Most tackles in a football game are executed by the same few defensive players. Likewise, most field goals in a basketball game are scored by the same few offensive players. Same in hockey, soccer, other sports

• A few metropolises are home to the majority of humans, while a plethora of smaller cities and villages house the rest

• About 65 percent of all businesses don’t make it past the ten-year mark, creating relatively little revenue. Of the remaining 35 percent, only a fraction creates most of the total revenue

• Web traffic: It is estimated that 90 percent of all websites don’t receive any organic traffic, while the remaining 10 percent get it all.

But there are differences.

Pareto distributions are usually observed in large-scale phenomena such as  crop yields, investments or software problems. 

Price’s law focuses on social group settings. And, unfortunately, the bigger the group, the more incompetence is found. 

source: Kaguura Gichuru 

The Pareto Principle outcomes tend to scale linearly; Price’s Law outcomes scale exponentially. 

But keep in mind that Price’s Law refers only to quantitative outcomes, and does not address qualitative impact.

One scientist might publish 10 papers in one year, but with very little impact on the field, while another scientist might publish just one paper in 10 years and completely revolutionize the field.

The point is that AI is not yet “taking jobs.” 

Firms are shifting resource allocations to support AI infrastructure, yes. Yet even so, Price’s Law suggests that a thoughtful rebalancing might not affect firm productivity.

Magnifica Humanitis: Balancing AI and its Human Ends

Pope Leo XIV’s first encyclical Magnifica Humanitas, concerning artificial intelligence, already is widely compared to Rerum Novarum, issued in May 1891. 

To be sure, it can be argued that all encyclicals since Rerum Novarum have been about Catholic social teaching. “Magnifica Humanitas” addresses AI in that context. 

Rerum Novarum addressed the rights and duties of capital and labor during the Industrial Revolution. Its main points include defending private property rights, advocating for workers' dignity and living wages, upholding the right to form unions, and emphasizing the state's obligation to protect the vulnerable while rejecting both unrestrained capitalism and socialism.

Magnifica Humanitas might have importance as one of the first major global religious/social statements treating AI as a civilization-scale issue comparable to the Industrial Revolution.

As always, Catholic social doctrine is based on the dignity of the human person. So the document emphasizes AI in the context of the primacy of the human person and human dignity, as well as the need to subordinate technology to human ends. 

It also is fair to note that encyclicals are often read through the lens of the reader’s prior commitments, so people tend to notice the parts that confirm what they already think and dismiss the rest.

Encyclicals are pastoral and argumentative documents, so they are written to persuade, guide, or correct on issues that are already contested. That means readers who come in already aligned with the pope will often see clarity and continuity, while critics may see ambiguity, overreach, or hidden agendas.

In practice, that makes an encyclical a kind of mirror: it can reflect the reader’s assumptions as much as the author’s intent.

Conservatives would likely read Magnifica Humanitas as a defense of human dignity, family, labor, and limits on technocratic power.

Liberals would likely see a call for regulation, solidarity, and protecting vulnerable people from AI harms. 

Pro-AI readers would probably emphasize that it is not anti-technology but an attempt to steer AI toward the common good. 

Anti-AI readers would focus on its warnings about dehumanization, manipulation, and the erosion of responsibility.

Theme	Conservative interpretation	Liberal interpretation	Pro-AI interpretation	Anti-AI interpretation
Labor	Protects workers from displacement, deskilling, and elite technocrats; favors stable human-centered work f	Supports worker protections, retraining, and limits on corporate power; emphasizes inequality and labor rights.	AI can augment productivity, raise wages, and create new kinds of work if deployed responsibly.	AI is a direct threat to employment, bargaining power, and the dignity of work
Regulation	Accepts guardrails if they preserve moral order and national/community control; resists bureaucratic overreach	Sees strong public oversight as necessary for safety, fairness, and accountability	Prefers light-touch or targeted rules that enable innovation while reducing abuses	Wants aggressive limits, moratoria, or bans on especially risky AI uses.
Transhumanism	Rejects it as a threat to the integrity of human nature and created limits	Critiques it when tied to inequality, commodification, or loss of human solidarity	Treats it as one possible frontier, but insists enhancement must remain ethically bounded	Sees it as a dangerous form of hubris that replaces persons with optimized systems
Human dignity	Grounds dignity in the irreducible worth of the person, family, and moral order.	Grounds dignity in rights, inclusion, and protection from exploitation or algorithmic harm	Argues dignity is preserved when AI serves human agency rather than replacing it	Argues dignity is already endangered by automation, surveillance, and machine-mediated life

The encyclical is a balancing act, to be sure. As Rerum Novarum had to steer between socialism and secular individualism (also humans as commodities) as contrary to Catholic values, so Magnifica Humanitas strives to balance technology and its subordination to human needs and values.

Actually, AI Probably Isn't Taking Many Jobs, Yet

Headlines aside, there is arguably much less artificial intelligence job displacement than actually is the case, though some might argue for one particular nuance.

And that nuance is that AI is not actually displacing jobs when large enterprise job cuts are announced. Instead, a redirection of spending is envisioned, with the savings on labor being redeployed to support AI infrastructure creation. 

We can argue about whether that actually represents active AI substitution for existing labor, or is mostly a financial move designed to redirect resources to support a huge capital investment wave. 

source: Bret Jensen 

But the “fact” is that such job cuts are not really about AI displacing an existing job. For example, many firms overhired during the labor shortages caused by the Covid pandemic, and are now simply rebalancing. 

Some might call all of this "AI washing,” a strategic financial pivot disguised as an actual immediate substitution of AI for human labor:

• Capital reallocation: Companies cut headcount to free up the massive capital required for AI infrastructure, GPU compute, and model training

• Narrative: Frame a difficult, necessary financial correction as a visionary, tech-driven transformation.

• Efficiency: Reducing headcount in areas that were already overstaffed.

Company	Context of Announcement	Strategic Driver	Source
Meta	Workforce reductions linked to AI restructuring and operations simplification.	Reorganizing toward AI infrastructure; correcting over-hiring.	Financial Express
Amazon	Linked layoffs to efficiency measures and "AI-forward" operations.	Leaner management; funding heavy AI/cloud investment.	Medium
Block	Shrinking workforce citing AI tools accelerating productivity.	Reconfiguring to capitalize on strategic AI priorities.	CBS News
Pinterest	Cuts made to deliver on an "AI-forward" strategy.	Hiring new AI-proficient talent while reducing legacy roles.	CBS News
Cisco	"Hard decisions" made to shift investment toward AI era competitiveness.	Strategic discipline and focus on AI infrastructure.	NSJ Online
Klarna	Initially cited AI for headcount reductions; reportedly faced quality declines.	Experimentation with AI-led efficiency (with evidence of re-hiring).	Reddit (AI Tracking)

A reporting requirement by the state of New York whenever mass layoffs are conducted does not support the notion that AI is responsible for big layoffs there. 

source: Ahmed Abdelfattah

 

“In 2025, the New York Department of Labor updated the state’s Worker Adjustment and Retraining Notification Act system, asking businesses to disclose whether layoffs are related to artificial intelligence,” note Robert Quackenboss, Hinton partner and Michelle Meyer, Hinton associate. “In the year since the change took effect, no business has reported AI as a reason for layoffs.”

“Even though more than 160 different companies have filed WARN notices with the NYS DOL, not a single notice has attributed layoffs to AI technology or automation,” they say. 

To be sure, company resources are being diverted to capex and AI opex, and that is made possible by reductions of force. 

But AI is only an indirect cause, and not because it actually is being used to replace current human labor. 

The immediate cause is a need to invest resources in AI infrastructure. As with any firm resource allocation, there is a zero-sum element: what gets spent in one area means less spending somewhere else.